Correlated Non-Parametric Latent Feature Models
This work addresses the problem of modeling correlated hidden factors in data for researchers and practitioners in machine learning, representing an incremental extension of the IBP.
The authors tackled the limitation of the Indian Buffet Process (IBP) in assuming uncorrelated latent features, which is inadequate for real-world problems, by introducing a framework for correlated nonparametric feature models that generalizes the IBP and demonstrated its applications on real-world datasets.
We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.